Sarcopenia en pacientes de hospital de día geriátrico / Sarcopenia in a geriatric day hospital

Introducción: La sarcopenia es una enfermedad muscular progresiva y generalizada asociada con un aumento de los resultados adversos para la salud (caídas, fracturas, discapacidad y mortalidad). Multiplica por 4 el riesgo de muerte por cualquier causa y tiene un gran impacto en otros resultados de salud y pérdida de calidad de vida. Objetivo: El objetivo principal de esta investigación es establecer la prevalencia y las variables relacionadas con la sarcopenia en pacientes de un hospital de día geriátrico. Metodología: Muestra de 55 pacientes: 40 mujeres (73%) y 15 hombres (27%), con una edad media de 73,25 años (desviación estándar de 13,4). Resultados: El 87% de los pacientes sobreviven al año de seguimiento. El coeficiente de correlación (positivo) (p < 0,01) para SARC-F y SPPB, SARC-F e índice de Barthel, y dinamómetro e índice de Barthel. El coeficiente de correlación de Pearson (negativo) (p < 0,05) para edad y medicación, índice de fragilidad e índice de Barthel, índice de fragilidad y GDS, e índice de Barthel y SPPB. Conclusiones: se puede concluir que el principal factor de riesgo para sarcopenia es la edad. Cuanto mayor es la edad, mayor es el riesgo de sarcopenia. En los mayores de 80 años se obtiene una alta prevalencia en comparación con otros estudios. La sarcopenia y la fragilidad se consideran fuertes predictores de morbilidad, discapacidad y mortalidad en las personas mayores (AU)

Introduction: Sarcopenia is a progressive and generalized muscledisease associated with an increase in adverse health outcomes (falls, fractures, disability and mortality). It is a disease that multiplies by 4 the risk of death from any cause and has a great impact on other health outcomes and loss of quality of life. Objective: The main objective of this research is to establish the prevalence and variables related to sarcopenia in patients from the geriatric day hospital. Methodology: Sample of 55 patients: 40 women (73%) and 15 men (27%), with a mean age of 73.25 years (standard deviation of 13.4). Results: The 87% of patients survive at one-year follow-up. The Pearson correlation coefficient (positive) (p < 0.01) for SARC-F and SPPB, SARC-F and Barthel index, and dynamometer and Barthel index. The Pearson correlation coefficient (negative) (p < 0.05) for age and medication, frailty index and Barthel index, frailty index (IFVIG) and GDS, and Barthel index and SPPB. Conclusions: it can be concluded that the main factor for sarcopenia is age. The older the age is, the greater the risk for sarcopenia. In those over 80 years of age, we obtain a high prevalence compared to other studies. Sarcopenia and frailty are considered strong predictors of morbidity, disability, and mortality in older people (AU)

Analysis of Risk Factors in Dementia Through Machine Learning.

BACKGROUND: Sociodemographic data indicate the progressive increase in life expectancy and the prevalence of Alzheimer's disease (AD). AD is raised as one of the greatest public health problems. Its etiology is twofold: on the one hand, non-modifiable factors and on the other, modifiable. OBJECTIVE: This study aims to develop a processing framework based on machine learning (ML) and optimization algorithms to study sociodemographic, clinical, and analytical variables, selecting the best combination among them for an accurate discrimination between controls and subjects with major neurocognitive disorder (MNCD). METHODS: This research is based on an observational-analytical design. Two research groups were established: MNCD group (n = 46) and control group (n = 38). ML and optimization algorithms were employed to automatically diagnose MNCD. RESULTS: Twelve out of 37 variables were identified in the validation set as the most relevant for MNCD diagnosis. Sensitivity of 100%and specificity of 71%were achieved using a Random Forest classifier. CONCLUSION: ML is a potential tool for automatic prediction of MNCD which can be applied to relatively small preclinical and clinical data sets. These results can be interpreted to support the influence of the environment on the development of AD.